RAG & Knowledge Systems for Retail

Retailers run on a sprawling, fast-changing catalog plus policies, promotions, and support history, and shoppers and agents need accurate answers at peak-season scale. A support bot that invents a return policy or a stale product detail costs trust and revenue, especially on a Black Friday spike. RAG fits because it grounds answers in your live catalog and policy and cites them, with freshness rules so changing prices and promotions stay correct. We build retrieval that handles peak demand, respects PCI-DSS and CCPA boundaries around payment and consumer data, and powers support and personalization with sourced, current answers.

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How we deliver it

RAG & Knowledge Systems, built for retail

We ingest catalog, policy, promotion, and support content with freshness rules, so retrieval reflects current prices, stock, and terms.

Hybrid retrieval returns the right product or policy, and answers cite the source so agents and shoppers can trust what they see.

We engineer for peak-scale demand with caching and an architecture that holds up through promotional spikes without degrading.

We keep payment and personal data inside PCI-DSS and CCPA boundaries, and an eval harness scores accuracy on real shopper questions per release.

Where it pays off in retail

Customer support answers

Resolve shopper questions on orders, returns, and policy with cited, current answers, deflecting tickets at peak volume.

Product discovery

Answer detailed catalog questions grounded in live product data, helping shoppers find the right item and lifting conversion.

Agent assist

Give support agents instant cited answers from policy and order history, cutting handle time during busy periods.

Personalized recommendations

Ground recommendations and answers in catalog and history while keeping consumer data within CCPA boundaries.

Support deflection rises and handle time falls during peak season, with every answer grounded in the current catalog and policy instead of a stale or invented one.

Retail AI, answered

We build freshness rules into ingestion, so retrieval reflects current catalog, pricing, and promotion data. Every answer cites its source, so shoppers and agents are working from the live record, not a stale snapshot.

Yes. We engineer the retrieval layer for peak-scale demand with caching and an architecture that absorbs promotional spikes. The system holds answer quality and latency steady when volume surges.

The system runs in your own cloud and keeps payment data inside PCI-DSS boundaries and personal data within CCPA controls. Sensitive consumer information stays scoped and never leaks through a shared answer.